AI evidence extraction

The influence of AI text generators on critical thinking skills in UK business schools

Aniekan Essien, Oyegoke Teslim Bukoye, Xianghan O’Dea, Marios Dominikos Kremantzis · Studies in Higher Education · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

1/4

Quality (LMQS)

Evidence

191

Citations

342.63

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/03075079.2024.2316881

Methodology & findings

Study design

Mixed-method research design employing both qualitative and quantitative data collection from 107 postgraduate business school participants in the UK

Sample

N = 107

Primary method

Mixed-method design (qualitative and quantitative components). Specific statistical methods and software packages are not detailed in the abstract.

Main result

The study found that "the most significant improvements occurred at the lower levels of Bloom's taxonomy." The research also identified concerns relating to reliability, accuracy, and potential ethical implications of AI text generator application in higher education, while demonstrating that generative AI technologies influence critical thinking skills in postgraduate business students.

Reports effect sizes.

Research paradigm

Mixed methods (pragmatist/interpretivist-positivist)

Author conclusions

The authors conclude that "this article serves as a guide to educators and policymakers, stressing the importance of a comprehensive approach to fostering critical thinking and other transferable skills in the higher education landscape." They emphasize that "the significance of this paper spans across, pedagogy, policy and practice, offering insights into the complex relationship between AI technologies and critical thinking skills."

Risk of bias

Not explicitly stated in the abstract; potential selection bias (UK postgraduate business school students only), possible self-selection bias, and lack of control group specification; Single institution study (UK business schools) - potential selection bias and limited generalizability; Sample size of 107 is relatively modest for generalizable conclusions; Potential self-selection bias if participation was voluntary; No mention of control group in abstract - unclear if comparison group was used; No mention of blinding or allocation concealment; Selection bias: Sample limited to UK postgraduate business school students, limiting generalizability; Self-selection bias: Participants volunteered for study involving AI technology; Measurement bias: Reliance on Bloom's taxonomy framework may not capture all dimensions of critical thinking; Confounding variables: Student prior experience with AI, academic ability, and prior critical thinking skills not explicitly controlled

Open questions raised

The paper identifies the need for further research on comprehensive approaches to fostering critical thinking and transferable skills in the context of AI technology adoption in higher education
The paper identifies the need for comprehensive approaches to integrating AI technologies in higher education while maintaining rigorous critical thinking instruction. The authors highlight gaps in understanding how to effectively use AI text generators without compromising the development of higher-order cognitive skills beyond the lower levels of Bloom's taxonomy.
The authors identify the need for comprehensive approaches to fostering critical thinking skills in the context of AI technologies. They highlight the importance of addressing ethical implications and developing pedagogical strategies that leverage AI while maintaining higher-order critical thinking development.

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